Health, community and development : towards a social psychology of participation

Population ageing is one of the major contemporary issues facing societies across the world. Originally framed as a major social and economic challenge, demographic ageing is now beginning to be seen as offering huge potential to individuals as well as to their communities. It is this positive potential that we explore in this issue by utilising two key disciplinary approaches—social gerontology and social/community psychology. In this introduction, we argue that focus on only one or the other of these perspectives is limiting. Instead, a more critical approach is needed that incorporates the strengths of both disciplines in order to build a more complete and stronger understanding of ageing and community. Thus, a focus on social gerontology highlights ageing issues and explores the diversity of older people and their interactions with community. By incorporating a social/community psychology approach, there is potential to complement this body of work through a deeper level of analysis around community, as well as individual and relational dimensions. The result is a special issue that brings together these two perspectives to address some of the shortcomings of approaching ageing through solely one disciplinary lens

Doctor of Philosophy in Computer Science

dissertationThe organization of learning materials is often limited by the systems available for delivery of such material. Currently, the learning management system (LMS) is widely used to distribute course materials. These systems deliver the material in a text-based, linear way. As online education continues to expand and educators seek to increase their effectiveness by adding more effective active learning strategies, these delivery methods become a limitation. This work demonstrates the possibility of presenting course materials in a graphical way that expresses important relations and provides support for manipulating the order of those materials. The ENABLE system gathers data from an existing course, uses text analysis techniques, graph theory, graph transformation, and a user interface to create and present graphical course maps. These course maps are able to express information not currently available in the LMS. Student agents have been developed to traverse these course maps to identify the variety of possible paths through the material. The temporal relations imposed by the current course delivery methods have been replaced by prerequisite relations that express ordering that provides educational value. Reducing the connections to these more meaningful relations allows more possibilities for change. Technical methods are used to explore and calibrate linear and nonlinear models of learning. These methods are used to track mastery of learning material and identify relative difficulty values. Several probability models are developed and used to demonstrate that data from existing, temporally based courses can be used to make predictions about student success in courses using the same material but organized without the temporal limitations. Combined, these demonstrate the possibility of tools and techniques that can support the implementation of a graphical course map that allows varied paths and provides an enriched, more informative interface between the educator, the student, and the learning material. This fundamental change in how course materials are presented and interfaced with has the potential to make educational opportunities available to a broader spectrum of people with diverse abilities and circumstances. The graphical course map can be pivotal in attaining this transition

Quantifying Structural Attributes of System Decompositions in 28 Feature-oriented Software Product Lines: An Exploratory Study

Background: A key idea of feature orientation is to decompose a software product line along the features it provides. Feature decomposition is orthogonal to object-oriented decomposition it crosscuts the underlying package and class structure. It has been argued often that feature decomposition improves system structure (reduced coupling, increased cohesion). However, recent empirical findings suggest that this is not necessarily the case, which is the motivation for our empirical investigation. Aim: In fact, there is little empirical evidence on how the alternative decompositions of feature orientation and object orientation compare to each other in terms of their association with observable properties of system structure (coupling, cohesion). This motivated us to empirically investigate and compare the properties of three decompositions (object-oriented, feature-oriented, and their intersection) of 28 feature-oriented software product lines. Method: In an exploratory, observational study, we quantify internal attributes, such as import coupling and cohesion, to describe and analyze the different decompositions of a feature-oriented product line in a systematic, reproducible, and comparable manner. For this purpose, we use three established software measures (CBU, IUD, EUD) as well as standard distribution statistics (e.g., Gini coefficient). Results: First, feature decomposition is associated with higher levels of structural coupling in a product line than a decomposition into classes. Second, although coupling is concentrated in feature decompositions, there are not necessarily hot-spot features. Third, the cohesion of feature modules is not necessarily higher than class cohesion, whereas feature modules serve more dependencies internally than classes. Fourth, coupling and cohesion measurement show potential for sampling optimization in complex static and dynamic product-line analyses (product-line type checking, feature-interaction detection). Conclusions: Our empirical study raises critical questions about alleged advantages of feature decomposition. At the same time, we demonstrate how the measurement of structural attributes can facilitate static and dynamic analyses of software product lines. (authors' abstract)Series: Technical Reports / Institute for Information Systems and New Medi

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

Networks and navigation in the knowledge economy: Studies on the structural conditions and consequences of path-dependent and relational action

In the wake of a relational turn, economic geographers have begun to scrutinize the relationships and interactions between people and organizations as a driving force behind economic processes at both global and local scales. Through a focus on contingent contextuality and path dependence, relational economic geography and network thinking have provided the necessary conceptual toolbox for untangling the structural effects and drivers of these relationships and their spatial embeddedness. However, despite the conceptual richness of the relational approach, empirical studies have often fallen short of capturing its core tenets: First, there is a prevalence to focus on places, infrastructures, and similarities as aggregate proxies for actors and their socio-economic relationships as the unit of geographical network analysis; While often convenient, this approach misses out on the capacity of networks to represent spatially embedded social contexts as enablers or constraints of economic action. Second, while path dependence is at the heart of evolutionary approaches towards economic geography, few studies actually trace how path-dependent and interrelated innovation shapes the long-term emergence of fields. Relational processes are especially salient when outcomes are opaque, decisions are interdependent, and when formal rules and roles are weak or absent. In this thesis, I ask how actors navigate such contexts and investigate the structural conditions and consequences of their navigation efforts. In my pursuit of this question, I draw on literatures from sociology, economics, and organization studies and build on novel methods of network analysis capable of empirically capturing contextuality and path dependence to investigate relational processes at three levels of economic activity: The thesis first looks towards a localized and informal trade platform to demonstrate how consumers rely on their former transactions to navigate exchange uncertainty and how such an exchange system can become liable to personal lock-in. It then moves on to show how the geographically and organizationally diversified search for innovation opportunities structures the transfer of knowledge across a globalized and partially informal corporate scouting community. Finally, the thesis shows how the linkage of distinct knowledge domains drives the long-term emergence of heterogeneous technological fields. In its endeavor to trace these processes, the thesis contributes a set of distinct relational research designs that demonstrate how advances in methods and data can be employed to empirically exploit the conceptual richness of relational economic geography